


import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class        lowerCAmelCase__ (					lowercase__  ):






						"""simple docstring"""
						__UpperCAmelCase       :						List[str]    	=			(KDPMaDiscreteScheduler,)
						__UpperCAmelCase       :						Optional[int]    	=			10




						def 	_UpperCamelCase					(       self ,				**a_      ):
							lowerCamelCase_					:				Optional[int]              =  {
							    "num_train_timesteps": 1100,
							    "beta_start": 0.00_01,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							}

							config.update(**SCREAMING_SNAKE_CASE_      )
							return config




						def 	_UpperCamelCase					(       self      ):


							for timesteps in [10, 50, 100, 1000]:
								self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_      )




						def 	_UpperCamelCase					(       self      ):


							for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01] ,				[0.00_02, 0.0_02, 0.02]      ):
								self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_ ,				beta_end=SCREAMING_SNAKE_CASE_      )




						def 	_UpperCamelCase					(       self      ):


							for schedule in ["linear", "scaled_linear"]:
								self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_      )




						def 	_UpperCamelCase					(       self      ):


							for prediction_type in ["epsilon", "v_prediction"]:
								self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_      )




						def 	_UpperCamelCase					(       self      ):
							lowerCamelCase_					:				List[str]              =  self.scheduler_classes[0]
							lowerCamelCase_					:				Tuple              =  self.get_scheduler_config(prediction_type="v_prediction"      )
							lowerCamelCase_					:				Dict              =  scheduler_class(**SCREAMING_SNAKE_CASE_      )

							scheduler.set_timesteps(self.num_inference_steps      )

							lowerCamelCase_					:				Any              =  self.dummy_model()
							lowerCamelCase_					:				List[Any]              =  self.dummy_sample_deter * scheduler.init_noise_sigma
							lowerCamelCase_					:				List[str]              =  sample.to(SCREAMING_SNAKE_CASE_      )

							for i, t in enumerate(scheduler.timesteps      ):
								lowerCamelCase_					:				Dict              =  scheduler.scale_model_input(SCREAMING_SNAKE_CASE_ ,				SCREAMING_SNAKE_CASE_      )

								lowerCamelCase_					:				Tuple              =  model(SCREAMING_SNAKE_CASE_ ,				SCREAMING_SNAKE_CASE_      )

								lowerCamelCase_					:				List[Any]              =  scheduler.step(SCREAMING_SNAKE_CASE_ ,				SCREAMING_SNAKE_CASE_ ,				SCREAMING_SNAKE_CASE_      )
								lowerCamelCase_					:				Dict              =  output.prev_sample

							lowerCamelCase_					:				List[Any]              =  torch.sum(torch.abs(SCREAMING_SNAKE_CASE_      )      )
							lowerCamelCase_					:				int              =  torch.mean(torch.abs(SCREAMING_SNAKE_CASE_      )      )

							if torch_device in ["cpu", "mps"]:
								assert abs(result_sum.item() - 4.6934E-07      ) < 1E-2
								assert abs(result_mean.item() - 6.1112E-10      ) < 1E-3
							else:
								# CUDA
								assert abs(result_sum.item() - 4.693_4286_5017_0972E-07      ) < 1E-2
								assert abs(result_mean.item() - 0.00_02      ) < 1E-3




						def 	_UpperCamelCase					(       self      ):


							if torch_device == "mps":
								return
							lowerCamelCase_					:				Any              =  self.scheduler_classes[0]
							lowerCamelCase_					:				Union[str, Any]              =  self.get_scheduler_config()
							lowerCamelCase_					:				Any              =  scheduler_class(**SCREAMING_SNAKE_CASE_      )

							scheduler.set_timesteps(self.num_inference_steps      )

							lowerCamelCase_					:				Dict              =  self.dummy_model()
							lowerCamelCase_					:				str              =  self.dummy_sample_deter * scheduler.init_noise_sigma
							lowerCamelCase_					:				Dict              =  sample.to(SCREAMING_SNAKE_CASE_      )

							for i, t in enumerate(scheduler.timesteps      ):
								lowerCamelCase_					:				str              =  scheduler.scale_model_input(SCREAMING_SNAKE_CASE_ ,				SCREAMING_SNAKE_CASE_      )

								lowerCamelCase_					:				str              =  model(SCREAMING_SNAKE_CASE_ ,				SCREAMING_SNAKE_CASE_      )

								lowerCamelCase_					:				Optional[int]              =  scheduler.step(SCREAMING_SNAKE_CASE_ ,				SCREAMING_SNAKE_CASE_ ,				SCREAMING_SNAKE_CASE_      )
								lowerCamelCase_					:				Union[str, Any]              =  output.prev_sample

							lowerCamelCase_					:				Dict              =  torch.sum(torch.abs(SCREAMING_SNAKE_CASE_      )      )
							lowerCamelCase_					:				List[Any]              =  torch.mean(torch.abs(SCREAMING_SNAKE_CASE_      )      )

							if torch_device in ["cpu", "mps"]:
								assert abs(result_sum.item() - 20.41_25      ) < 1E-2
								assert abs(result_mean.item() - 0.02_66      ) < 1E-3
							else:
								# CUDA
								assert abs(result_sum.item() - 20.41_25      ) < 1E-2
								assert abs(result_mean.item() - 0.02_66      ) < 1E-3




						def 	_UpperCamelCase					(       self      ):


							if torch_device == "mps":
								return
							lowerCamelCase_					:				Optional[Any]              =  self.scheduler_classes[0]
							lowerCamelCase_					:				Union[str, Any]              =  self.get_scheduler_config()
							lowerCamelCase_					:				Optional[Any]              =  scheduler_class(**SCREAMING_SNAKE_CASE_      )

							scheduler.set_timesteps(self.num_inference_steps ,				device=SCREAMING_SNAKE_CASE_      )

							lowerCamelCase_					:				List[str]              =  self.dummy_model()
							lowerCamelCase_					:				Union[str, Any]              =  self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE_      ) * scheduler.init_noise_sigma

							for t in scheduler.timesteps:
								lowerCamelCase_					:				Tuple              =  scheduler.scale_model_input(SCREAMING_SNAKE_CASE_ ,				SCREAMING_SNAKE_CASE_      )

								lowerCamelCase_					:				List[str]              =  model(SCREAMING_SNAKE_CASE_ ,				SCREAMING_SNAKE_CASE_      )

								lowerCamelCase_					:				List[Any]              =  scheduler.step(SCREAMING_SNAKE_CASE_ ,				SCREAMING_SNAKE_CASE_ ,				SCREAMING_SNAKE_CASE_      )
								lowerCamelCase_					:				Dict              =  output.prev_sample

							lowerCamelCase_					:				Optional[int]              =  torch.sum(torch.abs(SCREAMING_SNAKE_CASE_      )      )
							lowerCamelCase_					:				List[Any]              =  torch.mean(torch.abs(SCREAMING_SNAKE_CASE_      )      )

							if str(SCREAMING_SNAKE_CASE_      ).startswith("cpu"      ):
								# The following sum varies between 148 and 156 on mps. Why?
								assert abs(result_sum.item() - 20.41_25      ) < 1E-2
								assert abs(result_mean.item() - 0.02_66      ) < 1E-3
							else:
								# CUDA
								assert abs(result_sum.item() - 20.41_25      ) < 1E-2
								assert abs(result_mean.item() - 0.02_66      ) < 1E-3



"""simple docstring"""






import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar



class 			lowercase					( unittest.TestCase		):






			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	Optional[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  logging.get_logger()

				# the current default level is logging.WARNING
				lowerCAmelCase												=  logging.get_verbosity()

				logging.set_verbosity_error()
				self.assertEqual(logger.getEffectiveLevel()      ,logging.get_verbosity()			)

				logging.set_verbosity_warning()
				self.assertEqual(logger.getEffectiveLevel()      ,logging.get_verbosity()			)

				logging.set_verbosity_info()
				self.assertEqual(logger.getEffectiveLevel()      ,logging.get_verbosity()			)

				logging.set_verbosity_debug()
				self.assertEqual(logger.getEffectiveLevel()      ,logging.get_verbosity()			)

				# restore to the original level
				logging.set_verbosity(SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	Optional[int]:





				"""simple docstring"""


				lowerCAmelCase												=  logging.get_verbosity()

				lowerCAmelCase												=  logging.get_logger('''transformers.models.bart.tokenization_bart'''			)
				lowerCAmelCase												=  '''Testing 1, 2, 3'''

				# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
				if level_origin <= logging.WARNING:
					with CaptureLogger(SCREAMING_SNAKE_CASE_			) as cl:
						logger.warning(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(cl.out      ,msg + '''\n'''			)

				# this is setting the level for all of `transformers.*` loggers
				logging.set_verbosity_error()

				# should not be able to log warnings
				with CaptureLogger(SCREAMING_SNAKE_CASE_			) as cl:
					logger.warning(SCREAMING_SNAKE_CASE_			)
				self.assertEqual(cl.out      ,''''''			)

				# should be able to log warnings again
				logging.set_verbosity_warning()
				with CaptureLogger(SCREAMING_SNAKE_CASE_			) as cl:
					logger.warning(SCREAMING_SNAKE_CASE_			)
				self.assertEqual(cl.out      ,msg + '''\n'''			)

				# restore to the original level
				logging.set_verbosity(SCREAMING_SNAKE_CASE_			)






			@mockenv(TRANSFORMERS_VERBOSITY='''error'''			)
			def 					UpperCAmelCase							(self  :	Any			)       ->	str:





				"""simple docstring"""


				transformers.utils.logging._reset_library_root_logger()
				# this action activates the env var
				lowerCAmelCase												=  logging.get_logger('''transformers.models.bart.tokenization_bart'''			)

				lowerCAmelCase												=  os.getenv('''TRANSFORMERS_VERBOSITY'''      ,SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  logging.log_levels[env_level_str]

				lowerCAmelCase												=  logging.get_verbosity()
				self.assertEqual(
				    SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,F"""TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}"""      ,)

				# restore to the original level
				lowerCAmelCase												=  ''''''
				transformers.utils.logging._reset_library_root_logger()






			@mockenv(TRANSFORMERS_VERBOSITY='''super-error'''			)
			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	Tuple:





				"""simple docstring"""


				transformers.utils.logging._reset_library_root_logger()
				lowerCAmelCase												=  logging.logging.getLogger()
				with CaptureLogger(SCREAMING_SNAKE_CASE_			) as cl:
					# this action activates the env var
					logging.get_logger('''transformers.models.bart.tokenization_bart'''			)
				self.assertIn('''Unknown option TRANSFORMERS_VERBOSITY=super-error'''      ,cl.out			)

				# no need to restore as nothing was changed






			def 					UpperCAmelCase							(self  :	Dict			)       ->	Any:





				"""simple docstring"""


				transformers.utils.logging._reset_library_root_logger()

				lowerCAmelCase												=  logging.get_logger('''transformers.models.bart.tokenization_bart'''			)
				lowerCAmelCase												=  '''Testing 1, 2, 3'''

				with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='''1'''			):
					# nothing should be logged as env var disables this method
					with CaptureLogger(SCREAMING_SNAKE_CASE_			) as cl:
						logger.warning_advice(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(cl.out      ,''''''			)

				with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=''''''			):
					# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
					with CaptureLogger(SCREAMING_SNAKE_CASE_			) as cl:
						logger.warning_advice(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(cl.out      ,msg + '''\n'''			)





def __magic_name__	(				)					->						List[Any]:

	'''simple docstring'''




	disable_progress_bar()
	assert are_progress_bars_disabled()

	enable_progress_bar()
	assert not are_progress_bars_disabled()


